Much of the previous literature has studied the relationship between individual lifestyle factors and the health-related quality of life (HRQOL). However, only a few studies combined them to explore their relative importance to the HRQOL in the elderly. This study assesses the HRQOL of the urban, rural, and institutionalized Chinese elderly and explores the relative contributions of different lifestyle factors to their HRQOL. The SF-36v2 Health Survey, the WHOQOL-OLD module, and the socio-demographic and lifestyle questionnaire were utilized in this study. Hierarchical regression was performed in order to analyze the results. The physical and mental component scores of the SF-36v2 survey were 47.05 ± 9.95 and 54.92 ± 9.92, respectively. The total score for the WHOQOL-OLD module was 73.01 ± 11.99, with institutionalized persons reporting lower scores. For the physical component of the elderly participants’ HRQOL, the R2 value changed the most (0.116) when exercise-and-labor-related factors were added in. For the mental component, sleep-related (0.054), and leisure-time-activity-related factors (0.053) caused the largest change of the R2 value. For the elderly-specific HRQOL, measured by the WHOQOL-OLD module, the leisure-time-activity-related factors caused the largest change in the R2 value (0.119), followed by exercise-and-labor-related factors (0.078). Heterogeneity was present among the three subgroups. In sum, compared with their community-dwelling counterparts, the HRQOL of institutionalized older people was relatively poor and different lifestyle factors contributed to the HRQOL differently.
Background: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder that is influenced by both genetic and environmental factors. However, the etiology of PCOS remains unclear. Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of genetically determined metabolites (GDMs) on the risk of PCOS. We used summary level data of a genome-wide association study (GWAS) on 486 metabolites (n = 7,824) as exposure and a PCOS GWAS consisting of 4,138 cases and 20,129 controls as the outcome. Both datasets were obtained from publicly published databases. For each metabolite, a genetic instrumental variable was generated to assess the relationship between the metabolite and PCOS. For MR analysis, we primarily used the standard inverse variance weighted (IVW) method, while three additional methods-the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods-were performed as sensitivity analyses. Results: Using genetic variants as predictors, we observed a robust relationship between epiandrosterone sulfate (EPIA-S) and PCOS (P IVW = 0.0186, P MR−Egger = 0.0111; P Weighted−median = 0.0154, and P MR−PRESSO = 0.0290). Similarly, 3-dehydrocarnitine, 4-hydroxyhippurate, hexadecanedioate, and β-hydroxyisovalerate may also have causal effects on PCOS development. Conclusions: We identified metabolites that might have causal effects on PCOS development. Our study emphasizes the role of genetic factors underlying the causal relationships between metabolites and PCOS and provides novel insights through the integration of metabolomics and genomics to better understand the mechanisms involved in human disease pathogenesis.
Objective: Sleep disturbances have been recognized as a risk factor for obesity. This study used polysomnography records to investigate associations between sleep fragmentation and obesity. Methods: Objectively measured sleep fragmentation data recorded by in-home polysomnography, including total arousal index (ArI-total), ArI in rapid eye movement (REM) sleep (ArI-REM), ArI in non-REM sleep (ArI-NREM), sleep fragmentation index, sleep efficiency (SE), and wake after sleep onset (WASO), were based on the Sleep Heart Health Study (2,835 men and 2,888 women with a mean [SD] age of 63.2 [11.2] years). Multivariable regression analyses were used to examine the relationship between sleep fragmentation and obesity.Results: Multinomial logistic regression showed that participants with obesity have a significantly higher ArI-total (odds ratio [OR] 1.018; 95%
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